MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641105449 A) filed by Karpagam College Of Engineering; Karpagam Academy Of Higher Education; Dr. S. Ganeshmoorthy; and K Vignesh on September 02, 2026, for Ab-Xai-Driven Financial Fraud Detection Using Rnn Models.
Inventors include Dr. S. Ganeshmoorthy; and K Vignesh.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: Financial fraud has become a major challenge in modern digital and online transaction systems, demanding accurate and reliable detection mechanisms. Traditional fraud detection methods often fail to capture complex sequential patterns and lack transparency in decision-making. To address these limitations, this paper proposes an Attention-Based Explainable Artificial Intelligence (AB-XAI) driven financial fraud detection framework using Recurrent Neural Network (RNN) models.The proposed system integrates convolutional layers for feature extraction, RNN and GRU layers for learning temporal transaction behavior, and an attention mechanism to focus on the most relevant features contributing to fraud detection. Additionally, Explainable AI (XAI) techniques are incorporated to enhance model interpretability, allowing users to understand the reasoning behind each prediction. Data preprocessing, feature scaling, dimensionality reduction using PCA, and class imbalance handling are performed to improve model performance.Experimental evaluation conducted on real- world financial and cybersecurity datasets demonstrates that the proposed AB-XAI-RNN framework achieves superior accuracy compared to existing models, while maintaining explainability and robustness. The results confirm that the proposed approach is effective for real-time financial fraud detection and can be practically deployed as a web-based cybersecurity solution project.
Disclaimer: Curated by HT Syndication.
